Event and alert severity are extremly important information for an effective alert management and response. Severity information determine the speed of response, needed resource allocation and the action path taken. Naturally, critical alerts have higher priority than major alerts which again overrule minor alerts.
When talking about log management, search history is overlooked more often than not. Past searches can be used as part of log analysis and forensic analysis, but the main issue with this data is the speed of search which gets compromised as data volume gets greater. We will discuss some ways to get the best out of your saved searches and to speed up the search process.
As with any enterprise technology, there are benefits and challenges when creating IT environments in the public cloud. The benefits include cost savings and the ability to easily scale up and down (just to name a few).
Logs from a variety of different AWS services can be stored in S3 buckets, like S3 server access logs, ELB access logs, CloudWatch logs, and VPC flow logs. S3 server access logs, for example, provide detailed records for the requests that are made to a bucket. This is very useful information, but unfortunately, AWS creates multiple .txt files for multiple operations, making it difficult to see exactly what operations are recorded in the log files without opening every single .txt file separately.
Elasticsearch and the rest of the Elastic Stack are commonly used for log and metric aggregation in various environments, including Kubernetes. In addition, the Elastic Stack is frequently being used for uptime tracking, with Heartbeat, as well as Application Performance Monitoring (APM), with agents supporting common programming languages, including Java.
This article originally appeared in TechBeacon. Gartner first coined the term "AIOps" a few years ago to describe "artificial intelligence for IT operations," and over the last few years, IT operations monitoring tool vendors have begun incorporating AIOps features into their products. Now AIOps tools are commonplace, but many IT leaders remain cautious about using these relatively new capabilities.
January is a good time for bug-fixing and we've spent the last couple of weeks making small tweaks and improvements to Downtime Monkey. We've focused on the kind of bugs that aren't urgent but shouldn't be overlooked and aim to continue this for a couple more weeks before starting development of the next major feature. We'll document each change in its own post. First up is an improvement to the range of webpages available for monitoring: webpages with query strings can now be monitored...
In the olden days, we used to have to get logs by putting our agent on one machine at a time, like hitching a horse to a horse-drawn carriage. But now, we’ve got Kubernetes. It’s like a horse factory, and we’ve got more horses than we know what to do with. In this wild west of containerization, we could quickly end up underneath more logs than our old-timey agent could keep track of! But now there’s a new sheriff in town.
AWS has a lot of services, and they all generate logs. A lot of logs. We’ve worked hard to make sure you can capture logs from every source and service on AWS, and today we’re happy to announce the final piece of our AWS logging puzzle: LogDNA’s S3 Collector integration. It’s an easy-to-use Lambda function that lets you ingest any AWS logs that get dumped to S3 – like logs from CloudFront and ELB.
